Consumer photography collections often contain assets captured by smart phones, tablets, digital cameras, and video recording devices, where asset refers to any media asset, such as a digital still image, or a digital video file. These collections are stored on the capture devices, media from those capture devices, personal computers, personal computer storage devices, cloud based storage, and online social websites. These collections may span multiple family members, relatives, friends, neighbors, and anyone directly or indirectly connected via online social networks.
The task of gathering, organizing, and assembling assets in preparation for sharing with others can be quite difficult. For example, after returning from a family vacation, the family may decide to create a slideshow for display at an extended family reunion. The collection of assets includes a mixture of still, video, and audio content that spans one or more cameras, each of the family smart phones, the family tablet, assets that were shared on social websites, stock assets from online websites of the locale, and may even include online assets of themselves they chose to purchase from a professional photographer or theme-based photo locations such as amusement park rides, famous places, or with animals, bugs, and famous people.
Several product creation tools, such as HP's Photobook Lite for iPad or even walk-up kiosks such as Eastman Kodak's Picture Kiosk allow a consumer to import assets from multiple devices and input modalities to create an output product such as a collage, calendar, photobook, or digital video. Once the product creation tool imports the collection of assets from all the aforementioned storage locations, there are often too many assets to use in the output product. By assigning a fitness score to each asset, the top scoring assets can be suggested for product creation. Often there are several assets which are quite similar to one another. When assets are deemed to be similar enough, only the asset with the highest score of the group of similar assets is used in the product. Further, there needs to be a way to determine which assets are superior to others such that they may be displayed more prominently (larger, more centrally located, or more duration).
U.S. Pat. No. 5,694,484 to Cottrell, et al., entitled “System and method for automatically processing image data to provide images of optimal perceptual quality,” describes a system involving several image processing modules and a method for selecting an image processing parameter that will optimize image quality for a given digital image, using information about the image capture device and the intended image output device. The method involves calculating an image quality metric that can be expressed as a series of mathematical transformations. The parameters used to control the image processing modules are varied, the image quality metric is calculated for each permutation of the control parameters, and the control parameters setting which yielded the best value of the image quality metric are used to process the digital image. The method of Cottrell et al. is performed on an individual image basis and therefore does not include an assessment of the quality of the digital image in either a relative or absolute basis relative to other digital images.
U.S. Pat. No. 6,671,405 to Savakis, et al., entitled “Method for automatic assessment of emphasis and appeal in consumer images,” discloses an approach which computes a metric of “emphasis and appeal” of an image, without user intervention. A first metric is based upon a number of factors, which can include: image semantic content (e.g. people, faces); objective features (e.g., colorfulness and sharpness); and main subject features (e.g., size of the main subject). A second metric compares the factors relative to other images in a collection. The factors are integrated using a trained reasoning engine. The method described in U.S. Patent Application Publication No. 2004/0075743 by Chantani et al., entitled “System and method for digital image selection,” is somewhat similar and discloses the sorting of images based upon user-selected parameters of semantic content or objective features in the images. These approaches have the advantage of working from the images themselves, but have the shortcoming of being computationally intensive.
U.S. Patent Application Publication No. 2007/0263092 to Fedorovskaya, et al., entitled “Value index from incomplete data,” discloses an image administration system and method to compute value indices from different combinations of capture data, intrinsic image data, image usage data, and user reaction data. This approach has the advantage of using combined data to calculate a value metric, but has the shortcoming of not utilizing data relevant to aesthetic value.
U.S. Patent Application Publication No. 2008/0285860 to Datta, et al., entitled “Studying aesthetics in photographic images using a computational approach,” discloses an approach to compute the aesthetic quality of images in which a one-dimensional support vector machine is used to find features with a noticeable correlation with user aesthetic ratings. Then, automated classifiers are constructed utilizing a simple feature selection heuristic. Numerical aesthetic ratings are inferred.
U.S. Pat. No. 6,816,847 to Toyama, entitled “Computerized aesthetic judgment of images,” discloses an approach to compute the aesthetic quality of images through the use of a trained and automated classifier based on features of the image.
Ke, et al., in their article entitled “The design of high-level features for photo quality assessment” (Proc. Computer Vision and Pattern Recognition, pp. 419-426, 2006) disclose an approach to classify images as either “high quality professional photos” or “consumer snapshots.” A number of features are proposed: spatial distribution of edges, color distribution, hue count, blur, contrast, and brightness. This approach is useful, but also limited by the metric being binary.
U.S. Pat. No. 8,311,364 to Cerosaletti, et al., entitled “Estimating aesthetic quality of digital images,” discloses an approach to compute the aesthetic quality of images through the use of a trained and automated classifier based on features of the image.